Federated Learning (FL) is a new method for performing decentralized machine learning. By introducing LD, these problems are addressed by allowing models to learn together without a central authority managing personal medical information. It describes how FL improves data safety while maintaining the same analytical results in multiple healthcare institutions. It looks into issues of ethical, legal, and technical features of FL that show how it matches global privacy laws. By studying the methods behind encryption, differential privacy, and secure multiparty computation, we can prove that FL can improve digital healthcare security. In addition, the use of real-life case studies and implementation plans is covered. It shows how FL is useful and presents difficulties in clinical work. This chapter contributes a well-rounded view of FL as being essential for wingspan, reliability, and justice healthcare that uses AI responsibly.